The increasing ubiquity of wireless network access has motivated the creation of several methods aimed at identifying the location of a wireless client based on radio signal strength measurements. Although these location-based systems continue to improve in terms of accuracy and ease of use, prior efforts have not yet considered the use of the ambient wireless infrastructure to identify in a direct manner the dynamics of the client, such as its motion and velocity. The same signals used for inferring location can be used for inferring dynamics. The information about dynamics, in turn, are useful for helping to infer both the client location and context, in general. Direct access to knowledge about the motion of a client has implications for the best way to fuse a series of signals received over time. For instance, knowledge that a client is motionless would let a location algorithm fuse a set of estimates for the current location into a single estimate with higher certainty. Knowledge of whether a mobile device (and user associated with a mobile client) is in motion may be useful, for example, to provide a signal about if and how to alert a user with an important message. It may be preferred to withhold messages until a user has arrived at a location, or only to let the most important messages through when a user is moving. In another example, it may be preferred to compress a message through summarization or truncation when a user is moving, or raise the volume of an alerting modality, or increase the size of the text of a display.
Location information may be employed to find people, places, and objects of interest. Beyond providing access to the current status of people and items, location information can support presence-forecasting services, which services provide information about a user's future presence or availability. In other applications, location is also useful for identifying the best way to relay notifications to a user, given device availabilities and the cost of interruption associated with different contexts. Location information may also be harnessed for the task of marshalling a set of nearby devices or device components.
Outdoor applications can rely on decoding timing signals from GPS (Global Positioning Service) or GLONASS (Global Navigation Satellite System) satellite navigation systems to obtain high-confidence location information. Unfortunately, no comparably ubiquitous means of measuring location is available for indoor applications. Although specialized systems such as active badges or radio frequency identification (RFID) tags can work well indoors, their installation costs may be prohibitive—and they require users to carry an extra device.
A promising alternative to relying on such specialized location systems is to infer location by accessing signals generated by an existing IEEE 802.11 wireless infrastructure (hereinafter also denoted as “Wi-Fi”) of the building. Wi-Fi installations have been diffusing quickly into private and public spaces. In parallel, increasing numbers of mobile devices equipped with IEEE 802.11 network interface hardware or built-in Wi-Fi sensing are becoming available. As the Wi-Fi infrastructure becomes more ubiquitous, location techniques exploiting the ambient radio signals can grow with it, despite the fact that Wi-Fi was never intended for inferring location.
Developing methods for accessing device information from an existing IEEE 802.11 Wi-Fi networking infrastructure is attractive as the use of ambient signals and receivers bypasses the need for special broadcasting and sensing hardware. Prior efforts on ascertaining location from IEEE 802.11 wireless signals have relied on the construction of detailed models of transmission and burdensome calibration efforts, aimed at mapping signals to locations.
The capability to identify the location of wireless clients indoors by measuring signal strengths from multiple IEEE 802.11 access points is not new. Matching signal strength signatures is the same basic technique used by all location-from-802.11 techniques, including a first one, called RADAR. Using a manually calibrated table of signal strengths, the RADAR nearest-neighbor algorithm gave a median spatial error of approximately 2.94 meters. In follow-on work, this error was reduced to approximately 2.37 meters using a Viterbi-like algorithm. Further research also precomputed signal strength signatures using a model of radio propagation and a floor plan of the building. This reduced the calibration effort at the expense of increasing their median location error to 4.3 meters.
Another conventional system, and perhaps the most accurate IEEE 802.11 location system, used Bayesian reasoning and a hidden Markov model (HMM). This system took into account not only signal strengths, but also the probability of seeing an access point from a given location. Like other work, it was based on a manual calibration. The system explicitly modeled orientation and achieved a median spatial error of about one meter using calibration samples taken approximately every 1.5 meters (five feet) in hallways. Many additional conventional systems have been employed using, for example, signal-to-noise ratios instead of the more commonly used raw signal strengths, and a formula that was used for approximating the distance to an access point as a function of signal strength.
Wi-Fi-centric systems have several attractive features, including privacy of location information. All location computations can be performed on the client device, and the device does not need to reveal the identity of the user or other information to the wireless interfaces to the wired network. The combination of growing ubiquity of Wi-Fi infrastructure, existing capable client devices, and privacy solutions make IEEE 802.11a compelling way to identify location.
However, what is still needed is a Wi-Fi location-based system that requires less training time while providing additional information related to location dynamics.